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Influence of Visual and Haptic Feedback on the Detection of Threshold Forces in a Surgical Grasping Task. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3068934] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Design and Evaluation of Anthropomorphic Robotic Hand for Object Grasping and Shape Recognition. COMPUTERS 2020. [DOI: 10.3390/computers10010001] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We developed an anthropomorphic multi-finger artificial hand for a fine-scale object grasping task, sensing the grasped object’s shape. The robotic hand was created using the 3D printer and has the servo bed for stand-alone finger movement. The data containing the robotic fingers’ angular position are acquired using the Leap Motion device, and a hybrid Support Vector Machine (SVM) classifier is used for object shape identification. We trained the designed robotic hand on a few monotonous convex-shaped items similar to everyday objects (ball, cylinder, and rectangular box) using supervised learning techniques. We achieve the mean accuracy of object shape recognition of 94.4%.
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A Model-Based Sensor Fusion Approach for Force and Shape Estimation in Soft Robotics. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2020.3008120] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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